Abstract 5711: Blood-based early detection of non-small cell lung cancer using orphan noncoding RNAs
Authors: Mehran Karimzadeh, Jeffrey Wang, Aiden Sababi, Oluwadamilare I. Afolabi, Dung Ngoc Lam, Alice Huang, Diana R. Corti, Kristle C. Garcia, Seda Kilinc, Xuan Zhao, Jieyang Wang, Taylor B. Cavazos, Patrick Arensdorf, Kimberly H. Chau, Helen Li, Hani Goodarzi, Lisa Fish, Fereydoun Hormozdiari, Babak Alipanahi
Journal: Cancer Research (2023)

Abstract Background: Orphan non-coding RNAs (oncRNAs) are a novel category of small non-coding RNAs that are present in the tumor tissue and blood of people with cancer and largely absent in people without cancer. To examine the potential of using oncRNAs for early cancer detection via liquid biopsy, we assessed the oncRNA content of serum from people with and without non-small cell lung cancer (NSCLC) and developed a prediction model for NSCLC. Methods: A total of 540 serum samples were obtained from Indivumed (Hamburg, Germany) and MT Group (Los Angeles, CA) and divided into cohort A for training (150 NSCLC cases, 219 controls; female: 30.7%/36.1%; mean age: 67.9 ± 8.9/62.4 ± 9.2; ever-smoker: 95.3%/26.9%, respectively) and cohort B for internal validation (88 NSCLC cases, 83 controls; female: 40.9%/54.2%; mean age: 62.7 ± 9.2/54.1 ± 12.4; ever-smoker: 89.8%/6.0%, respectively). We used RNA isolated from 0.5 mL of serum to generate and sequence libraries at an average depth of 18.5 ± 6.5 million 50-bp single-end reads using next-generation sequencing. Previously, we created a large catalog of NSCLC oncRNAs found in 999 NSCLC tumor tissues and largely absent in 679 normal samples from The Cancer Genome Atlas (TCGA) smRNA-seq database. This catalog was distilled by removing smRNA species found in the serum of an independent cohort of 31 non-cancer donors to yield a final NSCLC oncRNA catalog of 81,004 distinct oncRNA species. This distilled catalog was the reference for identifying NSCLC oncRNAs in the present study. Using oncRNA data we generated by sequencing samples from cohort A, we trained a logistic regression model for predicting NSCLC presence. The model was then validated in cohort B. Results: From the 540 samples sequenced, we detected 64,379 oncRNAs from the distilled TCGA oncRNA catalog in at least one sample across both cohorts (A: 55,650, B: 47,539).Using 5-fold cross-validation, the AUC of the logistic regression model was 0.95 (95% CI: 0.93-0.97) for the training cohort, and was 0.98 (0.97-0.99) for the validation cohort. Sensitivities for detecting NSCLC at 95% specificity were 0.78 (0.69-0.86) for early stage (I/II) cancer and 0.75 (0.60-0.87) for late stage (III/IV) cancer in the training cohort, and 0.92 (0.83-0.98) and 1.0 (0.85-1.0), respectively, in the validation cohort. Conclusion: These results demonstrate the potential for accurate, sensitive, and early detection of NSCLC through sequencing the oncRNA content of a routine blood draw. The performance of the model trained on one cohort and internally validated in a separate cohort supports the generalizability of this approach in detecting NSCLC. Citation Format: Mehran Karimzadeh, Jeffrey Wang, Aiden Sababi, Oluwadamilare I. Afolabi, Dung Ngoc Lam, Alice Huang, Diana R. Corti, Kristle C. Garcia, Seda Kilinc, Xuan Zhao, Jieyang Wang, Taylor B. Cavazos, Patrick Arensdorf, Kimberly H. Chau, Helen Li, Hani Goodarzi, Lisa Fish, Fereydoun Hormozdiari, Babak Alipanahi. Blood-based early detection of non-small cell lung cancer using orphan noncoding RNAs. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5711.